Taming vision transformers for clinical laryngoscopy assessment.

Objective: Laryngoscopy, essential for diagnosing laryngeal cancer (LCA), faces challenges due to high inter-observer variability and the reliance on endoscopist expertise. Distinguishing precancerous from early-stage cancerous lesions is particularly challenging, even for experienced practitioners, given their similar appearances. This study aims to enhance laryngoscopic image analysis to improve early screening/detection of cancer or precancerous conditions.

Zero-Shot Human-Object Interaction Detection via Similarity Propagation.

Human-object interaction (HOI) detection involves identifying interactions represented as [Formula: see text] , requiring the localization of human-object pairs and interaction classification within an image. This work focuses on the challenge of detecting HOIs with unseen objects using the prevalent Transformer architecture. Our empirical analysis reveals that the performance degradation of novel HOI instances primarily arises from misclassifying unseen objects as confusable seen objects.

LCBM: A Multi-View Probabilistic Model for Multi-Label Classification.

Multi-label classification is an important research topic in machine learning, for which exploiting label dependencies is an effective modeling principle. Recently, probabilistic models have shown great potential in discovering dependencies among labels. In this paper, motivated by the recent success of multi-view learning to improve the generalization performance, we propose a novel multi-view probabilistic model named latent conditional Bernoulli mixture (LCBM) for multi-label classification.